1

Preliminary

Computer Usage and Demand for Paper/Paperboard Products

Lei Lei

&

Haizheng Li

School of Economics and Center for Paper Business and Industry Studies

Georgia Institute of Technology

2

COMPUTER USAGE AND DEMAND FOR PAPER/PAPERBOARD PRODUCTS

ABSTRACT

The purpose of this paper is to analyze the effect of computer usage on the

demand for paper and paperboard products. A log-linear model is developed to analyze

the effect of computer usage on the demand for four categories of paper, newsprint,

printing/writing paper, packaging paper and household/sanitary paper. The analysis is

divided into two parts. The first part is US monthly analysis. We create computer number

index as a measurement for computer usage. Monthly data (from Jan 1992 to Jun 2005)

are collected to estimate the effect for four categories of paper. The monthly estimation

results support the hypothesis that the increasing usage of computer has a significantly

negative effect on the demand for printing/writing paper, and a significantly positive

effect on the demand for packaging paper. But it doesn't provide enough evidence for the

effect on the demand for newsprint.

The second part is the yearly analysis on 16 main countries, which constitute the

major demand for paper/paperboard products and are countries with widespread usage of

computer. Using the yearly data from 1961 to 2002 and applying fixone model, we find

that computer usage has a significantly positive effect on demand for packaging paper.

The small difference in US monthly analysis and 16 countries’ yearly analysis may arise

from the different measures in computer usage, prices of paper/paperboard products, and

income.

Keywords: Computer, demand, income, fixone

3

1. Introduction

The emergence and development of information and communication technology

(ICT) is now playing a more and more important role in every aspect of world economy.

Many studies have been made to analyze its effect on various industries. However, little

research has been done on the influence of ICT on the paper and paperboard industry.

What we are interested in is the effect of ICT on the demand for paper and

paperboard products. According to FAO1, paper and paperboard products can be divided

into three categories: newsprint, printing & writing paper2, and other paper & paperboard.

In other paper & paperboard, there are three subcategories: household & sanitary paper3,

wrapping /packaging paper/board4, and other paper & paperboard N.E.S (not elsewhere

specified). Thus there are four main categories of paper and paperboard products:

newsprint, printing/writing paper, wrapping /packaging paper/board, and

household/sanitary paper.

1 All definitions and categories below are given by Food and Agriculture Organization of the United Nations (FAO). 2 It includes such papers as: bank note, bible or imitation bible, book and magazine, box lining and covering, bristols, calculator papers, computer paper, duplicating, envelope stock, folder stock, label, lithograph, manifold, offset, onionskin, photographic base paper, poster, stationery, tablet or block, tabulating card stock, typewriter.

Other printing and writing paper is composed of following subcategories: 1) Coated printing & writing paper: a. Coated Wood Containing printing & writing paper, b. Coated

Woodfree printing & writing paper; 2) Uncoated printing & writing paper: a. Uncoated Wood Containing printing & writing paper, b.

Uncoated Woodfree printing & writing paper 3 It includes types of creped and uncreped papers such as disposable tissues, facial tissue, napkin, sanitary wadding, toilet tissue toweling, wiper stock, etc. 4 It is composed of following subcategories:

1) Linerboard: a. Kraft Liner: including Unbleached and Bleached Kraft Liner, b. Other Linerboard 2) Fluting Medium: a. Semi-chemical Fluting Medium; b. Other Fluting Medium 3) Kraft Wrapping and Packaging: a. Sack Kraft; b. Other Kraft Wrapping and Packaging 4) Folding Boxboard: a. Pulp Based Folding Boxboard: including Bleached Chemical Pulp Folding

Boxboard, and Other Pulp Based Folding Boxboard; b. Recovered Paper Based Folding Boxboard 5) Other Wrapping and Packaging N.E.S.: a. Other Wrapping Paper; b. Other Packaging Paper

4

More specifically, newsprint refers to an uncoated paper mainly used for printing

newspapers. Other uses are inserts and flyers, newspaper supplements and directories.

The weight of a sheet of newsprint is between 40g/m2 and 57g/m2. 5 Printing/writing

paper is used for printing and writing. Wrapping /packaging paper/board is a kind of

paper used to pack and carry market products for protection of the goods.

Household/sanitary paper is a type of paper used for sanitary disposable purposes. This

basic knowledge of paper and paperboard categories is helpful in analyzing the different

effect of computer on the demand for paper/paperboard products.

Clearly ICT may have different effect on demand for different categories of

paper/paperboard products. The increasing use of ICT may enable people to read

electronic edition rather than the traditional hardcopy of newspapers, magazines, and

books, which may decrease the demand for newsprint and printing/writing paper.

Secondly, the use of ICT will make it easier to access more information and computer,

which may increase the demand for printing/writing paper. Thirdly, the growing-up use

of the internet for shopping (e-commerce), due to the convenience and quickness

associated with internet, may increase the demand for packaging paper since the demand

for shipment may rise. In spite of the fact that the increasing usage of ICT has great

impact on the demand for three categories of paper, it is easy to see that ICT may have no

impact on the demand for household and sanitary paper.

In this paper we discuss the effect of ICT in two various ways. On the one hand,

we use US monthly data to analyze the effect of computer usage on the demand for US

paper and paperboard products. One the other hand, we use long-period yearly data of 16

5 The definition is from Forest Products Association of Canada and Association of European Publication Paper Producers.

5

main countries to analyze the effect of ICT on the demand for paper and paperboard

products. Then we can compare these two results and see whether the effect is robust.

This paper is organized as follows. In section 2, we introduce our methodology

and review some relevant studies on paper and paperboard industry. Section 3 develops

the empirical models for the paper demand, taking computer number as one of the

explanatory variables. Section 4 briefly describes the data. Section 5 presents the

estimation results and makes comparisons. Section 6 concludes the paper and gives

possible extensions.

2. Methodology and Literature Review

Firstly, we use monthly data to analyze the relationship between demand and

computer usage in US paper and paperboard industry. The U.S. paper and paperboard

industry is a traditional industry and used to play an important role in the development of

U.S. economy. However, nowadays this industry is faced with many problems and not as

important as before. One of the problems is the effect of computers and internet usage on

the demand for paper/paperboard. Being a remarkable change in the past decade, the

widespread use of computers has affected so many aspects of human life, including

working, studying, entertainment, and shopping style.

What we are concerned in this paper is the effect of computers usage on the

demand for the paper and paperboard products. As we have discussed above, the

increasing usage of computer may has great impact on the demand for newsprint,

printing/writing paper and wrapping /packaging paper/board, while it may not affect the

demand for household/sanitary paper.

6

As can be seen from Figure 16 in the Appendix, it is obvious that the demands for

four categories of paper & paperboard products in the past forty-one years have changed

at different rates. Does computer usage contribute to these differences? This is the focus

of our analysis.

Secondly, we use yearly data of 16 main countries7 in a longer period to analyze

the relationship between demand and computer usage in the paper and paperboard

industry.

In the world yearly level analysis, what we need to point out is the countries we

are interested in. In this paper we select 16 countries to analyze the effect of ICT on

demand for paper and paperboard products. There are two reasons for the selection of

these 16 countries. The first reason is that these 16 countries account for more than 60%

of the world demand. The other reason is that these 16 counties have high or medium ICT

(See Hetemaki and Nilsson (2005)), which enable us to make statistical analysis.

Thirdly, these two results are compared to check the robustness of our model.

There are some relevant studies on the demand or production estimation of paper

industry. Zhang and Buongiorno (1997) developed a model to estimate the demand for

printing and publishing papers, and the data are during the period of 1960-1991. They use

a two-stage almost ideal demand system (AIDS) representing the consumer demand for

communication (stage one) and for printed materials, computers, and televisions and

radios (stage two). Their results suggest that printed materials and computers were luxury

6 This graph is drawn according to the data provided by FAO. 7 The 16 countries included in our analysis are: Australia, Austria, Canada, Denmark, Finland, France, Germany, Italy, Japan, Netherlands, New Zealand, Norway, Spain, Sweden, United Kingdom, and United States of America.

7

goods; the demand for printed materials was independent of the price of the computers

and slightly complementary of the demand for the televisions and radios.

Roger Brown and Daowei Zhang (2004) analyze the paper products supply in the

United States for the period of 1981-2001. They presented the product-level econometric

models using two stage least squares geometric distributed lag model in log-log form ,

and estimate the long-run and short-run output price elasticities, and the input elasticities.

Hetemaki and Obersteiner use new methods, Bayesian model, to compute

projections for US newsprint demand up to 2020, which allows industry experts’ prior

knowledge about the future demand for newsprint to be included in the projections. They

also use an ad hoc model which assumes that newsprint demand is a function of changes

in newspaper circulation.

Li, Luo, and McCarthy (2004) investigate the demand pattern and structural

changes during the China’s economic transformation, using instrumental variables

estimations, cointegration analysis and error-correction models. The data they used are

yearly data from the paper and paperboard industry in China.

Although these literatures are on demand or production of pulp and paper industry,

little has been done on the effect of computer usage on paper and paperboard industry. In

this paper we try to make some basic analysis of this problem. As the data on internet

usage are not long enough, we only analyze the effect of U.S. computer number on the

U.S. demand of paper. The next section specifies the model we use.

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3. Model Specification

Firstly, we derive the demand model for paper/paperboard products without

computer number. According to Chas-Amil & J. Buongiorno (2000), the demand for

paper and paperboard products is derived from the demand for final products, thus the

demand D is a function of its price P and output Y. Similar as assumed in relevant

studies, the model we adopted is derived from a cost minimization problem subject to a

Cobb-Douglas production function:8

*

*

,

*

min

. . ( ) ( )t t

D Ot t t t t

D O

b ct t t

C D P O P

s t Y a D O

= +

= (1)

where tC is the total cost for producing final products tY 9, *tD is the equilibrium demand

for paper/paperboard products at time t, tO is the other inputs at time t, D Ot tP and P

denotes the price of paper/paperboard products, and other inputs at time t respectively, a,

b and c are parameters.

Using the Lagrange method to solve this problem, the demand function is as

follows:10

1 1* ( ) ( ) ( )

c cDtb c b c b c b c

t tOt

PbD a Yc P

− −+ + + += (2)

8 According to Hetemäki and Obersteiner, the forest products, including newsprint, act as intermediate input in the production function. 9 tY is the production of final products, which is generally measured by GDP in related studies.

10 Applying the first order condition to *tD and tO , we get *

Dt tO

t t

P bOP cD

= . Plugging this equation into the

Cobb-Douglas production function, we obtain the demand function for paper after optimization computation.

9

Secondly, we extend this static model into a dynamic model, that is, we allow for

partial adjustment. We assume*

1 1

( )et t

t t

D DD D− −

= (3)

, where 1,t tD D− is the actual (i.e., observed) demand at time t-1 and t, and 0 1e≤ ≤ is the

adjustment speed.

Plugging equation (2) in equation (3), we obtain the dynamic model:

* 1 11 1( ) ( ) ( ) ( ) ( ) ( )

e ce ce eDe e etb c b c b c b c

t t t t tOt

PbD D D a Y Dc P

− −− −+ + + +− −= = (4)

Next, we try to include computer number in the above function. Since computer

number can be regarded as a shock which may affect the demand for paper/paperboard

products, we just add computer number as a part of the constant a. Hence, we obtain the

following model:

* 1 11 1( ) ( ) [ ( ) ] ( ) ( ) ( ) ( )

e ec ec eDe e O d etb c b c b c b c

t t t t t tOt

PbD D D a COMP Y Dc P

− −− −+ + + +− −= = (5)

, where tCOMP is the number of computer a time t, Oa and d are parameters. Making

logarithmic transformation, we get

1

0 1 2 3 4 1

ln( ) ln( ) ln( ) ln( ) ln( ) ln( ) (1 ) ln( )

ln( ) ln( ) ln( ) ln( ) (6)

DO t

t t t tOt

t t t t

Pe ce b e ce deD a Y COMP e Db c b c c b c b c P b c

Y P COMP Dβ β β β β

−

−

= − + + − − + −+ + + + +

= + + + +

The last step is to add error terms tε , which leads to the empirical model:

0 1 2 3 4 1ln( ) ln( ) ln( ) ln( ) ln( )t t t t t tD Y P COMP Dβ β β β β ε−= + + + + + (7)

Since what we are interested in is the difference affect of computer usage on

different categories of paper/paperboard, the above model changes into:

10

0 1 2 3 4 , 1ln( ) ln( ) ln( ) ln( ) ln( )it t it t i t itD Y P COMP Dβ β β β β ε−= + + + + + (8)

, where the subscript i denotes newsprint, printing/writing paper, packaging paper and

household/sanitary paper respectively.

Equation (8) is the model that we use to analyze the effect of computer usage on

demand for different paper/paperboard categories. But in empirical analysis it is

necessary to test serial correlation of the error terms as it is time-series data. Therefore

this model may be revised to include more lagged variables according to the existence of

serial correlations arising from our data.

4. Data and Descriptive Statistics

(1) Monthly Data of US analysis

The range of monthly data is from January 1992 to June 2005. There are some

missing values in some specific months. The number of observations ranges from 132 to

162. Due to the availability of data, we use demand data of tissue as a substitute for the

demand data of household/sanitary paper in monthly analysis.

The monthly demand data for four categories of paper are apparent demand data,

which is calculated by adding production and import minus export. The monthly

production data is collected from ‘Pulp & Paper Week’. 11 The monthly import and

export data are calculated from the dataset of U.S. International Trade Commission.12

11 The production data of printing/writing paper is the sum of uncoated ground woods, coated papers, uncoated free sheet, and other printing/writing papers. 12 Since the trade data available are arranged in HS code, we construct a correspondence between HS code and four categories of paper/paperboard. Newsprint corresponds to HS four-digit code 4801(Newsprint); printing/writing includes HS four-digit code 4802 (Uncoated paper for writing, printing, office machines, excluding 480220 since its unit is square meters), 4804 (Uncoated kraft paper and paperboard) and 4805 (Uncoated paper and paperboard nes); packaging paper corresponds to HS code 4819 (Paper, board

11

The monthly price data we use are producer price index data, which are obtained

from the U.S. Bureau of Labor Statistics.13

As to the measurement of computer number, we define the number of computers

used at time t as the number of computers that have been sold in the past thirty-six

months, since computers are durable goods and normally the computer one bought three

years ago can still be used.14 And monthly data of computer number sold is calculated by

using the formula: monthly value of shipment of computer /monthly PPI of computer15,

where the monthly data on value of shipment of computer are obtained from U.S. Census

Bureau, and the monthly computer prices are price index data which are collected from

the U.S. Bureau of Labor Statistics. 16Thus the computer number we have created through

the above procedure is actually a computer number index. Its trend is shown in Figure 2

(Appendix).

When it comes to income, we choose industrial production value as a proxy for

income17, since it is the production value of all goods and closely related the total income

of the economy. 18 The data are obtained from Board of Governors of the Federal Reserve

System, Federal Reserve Bank19.

containers, packing items, box files, et); tissue includes HS code 4803 (Paper, household, sanitary, width >36 centimeters) and 4818 (Household, sanitary, hospital paper articles, clothing). 13 a. Newsprint, Series Id: WPU091302, not seasonally adjusted, base date: 8200; b. Packaging and industrial converting paper, Series Id: WPU091303, not seasonally adjusted, base date: 8200; c. Writing and printing papers, Series Id: WPU091301, not seasonally adjusted, base date: 8200; d. Sanitary paper products, including stock, Series Id: WPU091501, not seasonally adjusted, base date: 8200 14Computer number index in 1992, 1993 and 1994 are calculated according to the past three years’ yearly computer output index. 15 M3 series data, Item: 34A (Electronic Computer Manufacturing), Not Seasonally Adjusted. 16 Item: Electronic computers, Series Id: WPU115101, Not Seasonally Adjusted, base Date: 0412. 17 Most related studies use GDP to measure income, but in this paper we use monthly data and monthly GDP converted from quarterly GDP data are not accurate. So we just use industrial production value. 18 Another choice is disposable personal income, but we don’t use this one when taking into account that some percent of demand for paper products are from non-individual side (such as companies and governments). 19 Industrial production value is based on 2000 US dollars.

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The descriptive statistics of monthly data are shown in Table 1.

Figure 1 and Figure 2 in the Appendix show the US demand and computer index

change respectively.

(2) Yearly Data of 16 countries’ analysis

The apparent demand is calculated using the same method above, based on the

data collected from FAO dataset. 20 These are yearly data from 1961 to 2002.

As to real GDP, for the convenience of comparison, we use the data in 1995

constant dollars, which is from United Nations Common Database (UNCDB) and the

period is from 1961-2002. The total real GDP for the 16 countries as a whole is obtained

by summing up the country-level real GDP data. The yearly GDP data start from 1961

and end at 2002.

Under the assumption of perfect competition, the price can be approximated by

the import price. The nominal price for a country is calculated through dividing country’s

import value by import quantity. The country’s real price is calculated by using this

formula: Import Price=Import Value*GDP deflator/Import Quantity. Then the 16

countries’ average real price is the weighted sum of the real price data at country-level,

where we use the population share as the weight.21 The import value and quantity data are

obtained from FAO dataset and the GDP deflator are collected from UNCDB. Since the

20 Note: The production data for United States in year 1994 and 1995 are extremely small, with the value of 4286000 and 3879000, while the values in 1991, 1992, 1993,1996 and 1997 are 37929008, 39265008, 39874000, 47440000, and 49674000, respectively, all of which are one-digit longer than the values in 1994 and 1995. Therefore it is obvious that there is a mistake of missing one digit in these two years. In this paper we think that a last-order digit “0” is missing in the front of the values for 1994, and that a first-order digit “4” is missing in the front of the values for 1995, thus the production data for United States in year 1994 and 1995 are corrected into 42860000 and 43879000, respectively. Consequently the production and apparent data of Wrapping Paper/Paperboard category for world and North America Free Trade Area are corrected. 21 An alternative is to use demand share as weights, but it may cause the problem of endogeneity.

13

GDP deflator we found is for the period 1970-2002, the real price data is also for the

period 1970-2002.

The data for ICT, including number of personal computers22, internet users23, TV

receivers and mobile subscribers, are collected from UNCDB. The periods for these data

are 1980-2002, 1990-2002, 1980-1999, and 1980-2002, respectively. According to

Hetemaki and Nilsson (2005), ICT index= (Internet users+ mobile users+ PCs+

TVs)/1000 people.

Figure 3 and Figure 4 describe the total demand share and the total demand value

of the 16 countries from 1961 to 2002. Since household/sanitary paper is not the focus of

our analysis, we don’t include the change in this subcategory in the graphs. Thus the

categories shown in these two graphs are total paper/paperboard, newsprint,

printing/writing paper and wrapping/packaging paper/board. It is obviously that the

demand share is decreasing. For the total paper and paperboard products, the demand

share decreased from 81% in 1961 to about 64% in 2002. The changes in newsprint and

printing/writing paper are very similar as that of the total industry. For the

household/sanitary paper and wrapping/packaging paper/board, their demand shares are

more than 97% in 1961, while in 2002 it decreased to 67% and 62%, respectively.

Figure 5 and Figure 6 illustrate the change of nominal and real price of different

categories, respectively.

Thirdly, we relate the trend of demand with GDP. Figure 7 in the Appendix shows

the change of GDP and population of the 16 countries in the years from 1961 to 2002.It is

22 Number of Personal Computers (PC): measures the number of computers installed in a country. The statistic includes PCs, laptops, notebooks etc, but excludes terminals connected to mainframe and mini-computers that are primarily intended for shared use, 23 Internet is a linked world-wide network of computers in which users at any one computer, if they have permission, get information from other computers in the network.

14

clear that both the apparent demand and the GDP are increasing through the forty-one

years, and their patterns are very close.

Figure 8 describes the development of ICT in the 16 countries and the whole

world. It can be seen that the personal computers and internet users in the 16 countries

account for about 50% of the world. The shares of TV and mobile users in the 16

countries are about 40% in 2002.

The detailed descriptive statistics of yearly data of 16 countries data are shown in

Table 2 - Table 6. The first one is the information of the 16 countries, and the rest 4

tables are the statistics by categories.

5. Empirical Results

(1) US analysis

1) Estimation Using Monthly Data: 01/1993-06/2005

Firstly, due to the use of not-seasonally adjusted monthly data, we add eleven

monthly dummies into the model to take into account the impact of different months.

Secondly, since the lagged variable appears in the left side of the model, it may

result in a problem of dynamic incompletion if there is a serial correlation in the residuals.

To avoid this problem, we use monthly data and run a regression of OLS residuals on the

explanatory variables and the lagged residuals. The result (Table 1.2) shows that serial

correlation does exist with type AR(2). Consequently, we rewrite the model to include

three-ordered lagged demand variable.

The estimation results are reported in Table 1.3. It is clear that most monthly

dummies are significant in four models. For newsprint category, the coefficient for the

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variable of computer number index is negative and significant (-0.06 with t-value -3.95),

which means that the computer number index has a negative and significant effect on

demand for newsprint. This is accorded with the intuition that the increase of computer

usage will decrease the demand/production of newsprint since people may tend to get

information through computer while reducing the demand of hardcopy. The estimated

coefficient of Industrial production index, as a proxy of income, is positive and

significant. The effect of price is negative and significant in newsprint demand model.

As to printing/writing paper category, the computer number index has a negative

but insignificant effect on the demand. This result may be explained by the facts that

people may tend to read the electronic information and at the same time they may print

more materials since the information they have access are increasing. Therefore, the total

effect of computer number is ambiguous, and may be more towards negative due to the

gradual change of reading habit.

When it comes to packaging paper, the coefficient of computer number is positive

and insignificant, which may result from several factors. One point of view is that with

the widespread of e-communication, such as emails and e-cards, people tend to write

fewer mails. Consequently the increasing of computer number may decrease the

demand/production for packaging paper. On the other hand, with the blooming of internet,

e-commerce is developing, and this may increase the demand for packaging paper due to

less shopping in physical store. Thus the total effect is ambiguous and may be more

toward positive.

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As to demand model for household/sanitary paper, it’s not surprising that

computer number is insignificant. This result is reasonable since by intuition the number

of computers used wouldn’t affect the demand for household/sanitary paper.

In summary, the increasing of computer number may decrease the demand for

newsprint significantly, while it may not affect the demand for printing/writing paper

packaging paper, and household/sanitary paper. But the reasons for the insignificance

may differ.

2) Estimation Using Annual Data: 1961-2002

To make relatively longer time series comparison, we collected relevant annual

data to make regression. For the years before 1987, which is the earliest year for the

computer data, we simply give zero to the computer number. The procedure is the same

as that when we use monthly data.

First we test for serial correlation, and find that there is no indication of AR(1).

The results are reported in Table 2.2. This may arise from the fact that for annual data

one year lag is enough, while for monthly data one month adjustment is not enough.

Secondly we estimate the model with only one lagged demand variable. As can be

seen from Table 2.3.1 and Table 2.3.2, the computer number has a negative and

significant effect on the demand for newsprint, while it doesn’t seem to have affected

demand for other categories of paper/paperboard significantly. This result is similar as

what we find by using monthly data.

Generally speaking, the increasing use of computer and internet only has

significantly negative effect on demand for newsprint. As to printing/writing paper and

packaging paper, its effect is not significant, and this phenomenon may be altered with

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time going on. It is obvious that the living pattern of people has been changing gradually

due to increasing usage of computer. For instance, people may become more and more

accustomed to reading electronic materials instead of hardcopy magazines and

newspapers, which may decrease the demand for both newsprint and printing paper.

Another change is the springing up of e-commerce since the end of the 20th century,

which may stimulate the use of packaging paper.

(2) 16 countries’ analysis

Seeing the high correlation between computer and internet, we only include

computer into the model, and we use the data starting from 1981 to run regressions. Thus,

to analyze the effect of computer usage on the paper/paperboard industry, we use the

same model as in part (1), that is, including price, computer number, income(here is

GDP), one lagged demand variable, and time trend. Since this time we have 16 countries

instead of one country, fixone model is used instead of previous OLS regression.

The results of aggregate demand estimation are reported in Table 3.3.1 and Table

3.3.2, and the results of per capita demand estimation are reported in Table 3.4.1 and

Table 3.4.3, where we have tried both log-linear time trend and exponential time trend.

As can be seen from the tables, computer has a significantly positive effect on the

demand for printing and writing paper, while exponential time trend has a significantly

negative effect on the demand. For packaging paper, increased computer usage increases

the demand, while log-linear time trend has a significantly negative effect on the demand.

The results imply that the widespread usage of computer increase the demand for

printing/writing paper and packaging paper, which is in accordance with our analysis in

the production. On the other hand, as time goes on, people tend to use less

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printing/writing paper and packaging paper, which is also reasonable since people are

changing their way of living gradually. Another result is that neither computer usage nor

time trend has a significant effect on the demand for newsprint and household/sanitary

paper.

As to the per capita demand analysis, increasing computer usage increases the

demand for packaging paper, but the log-linear time trend will decrease the demand.

6. Conclusions

The purpose of this paper is to analyze the effect of computer usage on the

demand for paper and paperboard products. In general, we classified paper and

paperboard products into four categories: newsprint, printing/writing paper, packaging

paper and household/sanitary paper. In this paper we divide our analysis into two parts.

The first part is the analysis on US. Paper and paperboard industry is one of the

largest manufacturing industries in the United States. We collect relevant data and

develop a log-linear model to analyze the effect of computer usage on the demand for

paper. On the one hand, we use monthly data from Jan 1992 to Jun 2005 to estimate the

effect of computer usage on demand for four categories of paper. (Tissue is used as a

comparing category.) We create computer number index as a measurement for computer

usage. We also calculate monthly apparent demand data for four categories. The test

results show that there is serial correlation in the error terms, and we include three-

ordered lagged demand to correct for serial correlation. The estimation results support the

hypothesis that the increasing usage of computer has a significantly negative effect on the

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demand for newsprint. But it doesn't provide enough evidence for the effect on

printing/writing paper and packaging paper. The reason may be that there are both

positive and negative effects on these two categories, which lead to the insignificance of

total effect.

On the other hand, we also employ annual data from 1961 to 2002 to make

statistical analysis. Only first-order lagged demand is included in the model after the

serial correlation test. This may due to the adoption of annual rather than monthly data.

The estimation results are similar as above, and computer number index only affect the

annual demand for newsprint significantly.

The insignificant effect of computer number on printing/writing paper is due to

the negative effect resulted from reading electronic edition and the positive effect caused

by having access to more information and convenience to print. Thus the result may be

different several years later, since people’s reading habits are changing in an increasing

speed.

The second part is the analysis on 16 main countries, which constitute the major

demand for paper/paperboard products and are countries with widespread usage of

computer. Using fixone model, we find that computer usage has a positive effect on

demand for printing/writing paper and packaging paper, while time trend affects the

demand for these two categories of paper negatively. The above results hold for both

aggregate demand and per capita demand.

The difference in US analysis and 16 countries’ analysis may arise from the

different measures in computer usage, prices of paper/paperboard products, and income.

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This study calls for several possible extensions. One extension is to improve the

model specifications. Another improvement is related with finding better data on

computer numbers and demand for paper24, which would increase the accuracy of the

regression results.

24 Another available data on computer number is computer output index, which is a longer time series data from Jan 1972 to Oct 2004. And the basic results of using computer number index and compute production index are very close. Thus it may be possible to use compute production index as independent variable, since a larger sample size may be better for the analysis.

21

References

Buongiorno, J. (1978), “Income and Price Elasticities in the World Demand for Paper and

Paperboard”, Forest Science, 24, 231-246.

Baudin and Lundberg (1987), “The World Model of the Demand for Paper and

Paperboard”, Forest Science, 33, 185-196.

Chas-Amil, M. L. and J. Buongiorno (2000), “The Demand for Paper and Paperboard:

Econometric Models for the European Union”, Applied Economics, 32, 987-999.

David Tomberlin, Shushuai Zhu, Joseph Buongiorno(1998), “The global forest products

model (GFPM): users manual and guide to installation”, FAO Working Paper No:

GFPOS/WP/02.

Lauri Hetemäki and Michael Obersteiner, “US Newsprint Demand forecast to 2020,

Fisher Center for the Strategic Use of Information Technology”,

http://groups.haas.berkeley.edu/fcsuit/PDF-papers/LauriFisherPaper.pdf

Li, Luo, and McCarthy (2004), “Economics Transition and Demand Pattern: Evidence

from China’s Paper and Paperboard Industry”, working paper.

Roger Brown and Daowei Zhang ( 2004), “Paper Products Supply in the United States:

1981-2001”, working paper.

Simangunsong, B.C.H. and Buongiorno, J. (2001), “International Demand Equations for

Forest Products: A Comparison of Methods”, Scandinavian Journal of Forest

Research, 16: 155-172.

22

Torjus Folsland Bolkesjø, Michael Obersteiner, and Birger Solberg (2003), “Information

technology and the newsprint demand in Western Europe: a Bayesian approach”,

Can. J. For. Res., 33(9): 1644-1652 (2003)

Wooldridge, Introductory Econometrics- A Modern Approach, South-Western, College

Publishing.

Zhang and Buongiorno(1997), “Communication Media and Demand for

Printing/publishing Papers in the Unites States”, Forest Science, Vol. 43, No.3:

362-376.

23

Appendix:

US's Paper&Paperboard Demand:1961-2002

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

Millions Mt

Newsprint Printing/Writing Paper

Packaging Santiary Paper

Figure 1. US’s Paper/Paperboard Demand: 1961-2002

Computer Number Index:Jan 92-June 05

0

1000

2000

3000

4000

5000

6000

7000

Jan-92 May-93 Oct-94 Feb-96 Jul-97 Nov-98 Apr-00 Aug-01 Dec-02 May-04 Sep-05

Figure 2. Computer Number Index: Jan 92-June 05

24

60%

65%

70%

75%

80%

85%

90%

95%

100%

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

Year

Paper and Paperboard Newsprint

Printing+Writing Paper Wrapg+Packg Paper+Board

Figure 3. World Share of Paper/Paperboard Demand-16 Countries: 1961-2002

0

50

100

150

200

250

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

Millions Mt

Year

Paper and Paperboard Newsprint

Printing+Writing Paper Wrapg+Packg Paper+Board

Figure 4. Paper/Paperboard Demand-16 Countries: 1961-2002

25

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001year

$/ kg

Paper and Paperboard Newsprint

Printing+Writing Paper Wrapg+Packg Paper+Board

Figure 5. Nominal Price of Paper/Paperboard: 1961-2002

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002year

1990 US $/kg

Paper and Paperboard Newsprint

Printing+Writing Paper Wrapg+Packg Paper+Board

Figure 6. Real Price of Paper/Paperboard: 1970-2002

26

0

4

8

12

16

20

24

28

32

36

40

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

1995 US $ in Trillions

year

0

1000

2000

3000

4000

5000

6000

7000Millions

GDP-World GDP-16 CountriesPopulation-World Population-16 Countries

Figure 7. GDP & Population: 1961-2002

0

100

200

300

400

500

600

700

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Millions

year

PC-World PC-16 CountriesInternet-World Internet-16 Countries

Figure 8. Personal computers and Internet users: 1980-2002

27

Table 1: Descriptive Statistics- US Monthly Data Variable N Unit Mean Std Dev Min Max

Newsprint demand 160 1000 metric ton 983.91 85.46 771.73 1148.60 Printing/writing demand 159 1000 metric ton 1830.05 211.48 1001.98 2205.09 Packaging demand 135 1000 metric ton 275.18 24.36 212.08 329.29 Tissue demand 132 1000 metric ton 494.41 49.86 400.25 588.38

Industrial production value 162 Billion, in 2000 USD 2530.98 291.79 1951.95 2963.48

Value of shipment-Comp 162 million dollars 4549.29 1264.34 2522.00 7926.00 Computer number index 198 506.68 319.35 121.41 1245.37 Newsprint PPI 159 1982=100 128.58 20.12 98.90 186.20 Printing/writing PPI 162 1982=100 139.84 12.04 116.80 166.70 Packaging PPI 162 1982=100 162.19 13.37 137.20 185.20 Tissue PPI 162 1982=100 144.78 6.10 132.10 154.50

Table 2: Descriptive Statistics-16 Countries Information

Variable Unit N Mean Std Dev Min Max GDP billion $ 2499 1015.57 1588.49 27.48 9196.4 population billion 2538 0.05 0.06 0 0.29 GDPpercap $/person 2499 20948.69 7601.35 5511.82 44897.18 GDPdefla base 1990 2104 72.14 31.02 14.6 161.3 computer million 1016 11.93 25.93 0.1 190 internet million 824 7.55 20.8 0 159

Table 3: Descriptive Statistics of 16 countries- Newsprint Variable Unit N Mean Std Dev Min Max

demand million metric ton 654 1.3 2.57 0.01 13.13 dempercap kg/person 654 27.08 13.9 1.29 79.08 pricenominal $/ton 539 0.43 0.2 0.06 1.2 pricereal base 1990 445 0.72 0.22 0.13 1.97 Production metric ton 654 1377291.9 2156184.2 0 9970000 Export metric ton 654 733529.24 1812786.1 0 9029000 Import metric ton 654 655179.82 1571550.2 0 8142800 ImValue 1000 $ 654 284701.17 720671.59 0 4650760 ExValue 1000 $ 654 300198.58 814068.29 0 5638797

28

Table 4: Descriptive Statistics of 16 countries-Printing/Writing Variable Unit N Mean Std Dev Min Max

demand million metric ton 656 2.37 4.52 0.02 27.64 dempercap kg/person 656 43.92 27.99 4.3 213.27 pricenominal $/ton 656 0.72 0.34 0.17 1.88 pricereal base 1990 528 1.25 0.47 0.46 3.11 Production metric ton 656 2495082.6 4134408.5 0 24331000 Export metric ton 656 678194.78 1117233 0 7592020 Import metric ton 656 548788.52 948089.93 43 6490525 ImValue 1000 $ 656 441940.91 796498.84 25 5185090 ExValue 1000 $ 656 538297.55 913177.67 0 5442820

Table 5: Descriptive Statistics of 16 countries-Packaging Variable Unit N Mean Std Dev Min Max

Demand million metric ton 618 3.84 7.52 0.01 46.88 Dempercap kg/person 618 70.83 37.21 0.37 256.42 Pricenominal $/ton 580 0.56 0.27 0.11 1.45 Pricereal base 1990 502 0.91 0.37 0.26 4.71 Production metric ton 618 4090768.1 8042804.1 0 50675000 Export metric ton 618 771005.95 1108186.7 0 6774000 Import metric ton 618 517280.06 685733.26 0 3444000 ImValue 1000 $ 618 316740.96 492241.44 0 3063170 ExValue 1000 $ 618 459512 745553.75 0 4448808

Table 6: Descriptive Statistics of 16 countries- Household/Sanitary Variable Unit N Mean Std Dev Min Max

Demand million metric ton 610 0.55 1.12 0 6.51 Dempercap kg/person 610 10.09 6.41 0.02 33.51 Pricenominal $/ton 512 1.16 0.59 0.1 4.16 Pricereal base 1990 467 1.86 1 0.11 6.82 Production metric ton 610 552339.52 1115945.8 0 6447328 Export metric ton 610 30000.55 41878.65 0 327000 Import metric ton 610 26519.83 44036.97 0 264000 ImValue 1000 $ 610 30207.01 56142.36 0 459439 ExValue 1000 $ 610 32939.49 51114.13 0 364980